Long short-term memory neural network for air pollutant concentration predictions: Method development and evaluation
Air pollutant concentration forecasting is an effective method of protecting public health by
providing an early warning against harmful air pollutants. However, existing methods of air …
providing an early warning against harmful air pollutants. However, existing methods of air …
A novel spatiotemporal convolutional long short-term neural network for air pollution prediction
Air pollution is a serious environmental problem that has drawn worldwide attention.
Predicting air pollution in advance has great significance on people's daily health control …
Predicting air pollution in advance has great significance on people's daily health control …
RCL-Learning: ResNet and convolutional long short-term memory-based spatiotemporal air pollutant concentration prediction model
Predicting the concentration of air pollutants is an effective method for preventing pollution
incidents by providing an early warning of harmful substances in the air. Accurate prediction …
incidents by providing an early warning of harmful substances in the air. Accurate prediction …
A novel recursive model based on a convolutional long short-term memory neural network for air pollution prediction
Deep learning provides a promising approach for air pollution prediction. The existing deep
learning-based predicted models generally consider either the temporal correlations of air …
learning-based predicted models generally consider either the temporal correlations of air …
A novel Encoder-Decoder model based on read-first LSTM for air pollutant prediction
Accurate air pollutant prediction allows effective environment management to reduce the
impact of pollution and prevent pollution incidents. Existing studies of air pollutant prediction …
impact of pollution and prevent pollution incidents. Existing studies of air pollutant prediction …
Long short-term memory-Fully connected (LSTM-FC) neural network for PM2. 5 concentration prediction
People have been suffering from air pollution for a decade in China, especially from PM 2.5
(particulate matter with a diameter of less than 2.5 μm). Accurate prediction of air quality has …
(particulate matter with a diameter of less than 2.5 μm). Accurate prediction of air quality has …
Prediction of air pollutant concentration based on one-dimensional multi-scale CNN-LSTM considering spatial-temporal characteristics: A case study of Xi'an, China
H Dai, G Huang, J Wang, H Zeng, F Zhou - Atmosphere, 2021 - mdpi.com
Air pollution has become a serious problem threatening human health. Effective prediction
models can help reduce the adverse effects of air pollutants. Accurate predictions of air …
models can help reduce the adverse effects of air pollutants. Accurate predictions of air …
A novel hybrid model for six main pollutant concentrations forecasting based on improved LSTM neural networks
S Xu, W Li, Y Zhu, A Xu - Scientific Reports, 2022 - nature.com
In recent years, air pollution has become a factor that cannot be ignored, affecting human
lives and health. The distribution of high-density populations and high-intensity development …
lives and health. The distribution of high-density populations and high-intensity development …
[HTML][HTML] An LSTM-based aggregated model for air pollution forecasting
During the past few years, severe air-pollution problem has garnered worldwide attention
due to its effect on health and wellbeing of individuals. As a result, the analysis and …
due to its effect on health and wellbeing of individuals. As a result, the analysis and …
Multitask air-quality prediction based on LSTM-autoencoder model
X Xu, M Yoneda - IEEE transactions on cybernetics, 2019 - ieeexplore.ieee.org
With the development of the data-driven modeling techniques, using the neural network to
simulate the transport process of atmospheric pollutants and constructing PM 2.5 time-series …
simulate the transport process of atmospheric pollutants and constructing PM 2.5 time-series …